Machine Learning from Scratch using Python

Implementing classical ML models from scratch using only NumPy, and building small projects alongside for deeper understanding. No sklearn for model logic — just math and code.

Following the roadmap.sh/machine-learning roadmap — see ROADMAP.md for full progress tracker.

Progress

Category	Models	Status
Regression	Linear, Multiple, Lasso, Ridge, ElasticNet	Complete
Classification	KNN, Logistic Regression, SVM, Decision Trees, Random Forest	Complete
Unsupervised	K-Means, DBSCAN, PCA, ...	Up next

Models Implemented

Regression

Model	Code
Simple Linear Regression	SLR-byhand.py
Multiple Linear Regression	MLR-byhand.py
Ridge Regression	LR-ridge.py
Lasso Regression	LR-lasso.py
ElasticNet Regression	elasticnet_from_scratch.py

Classification

Model	Code
KNN (K-Nearest Neighbors)	KNN-byhand.py
Logistic Regression	log-regbyhand.py
SVM (Support Vector Machine)	SVM-byhand.py
Decision Trees	DecisionTrees-byhand.py
Random Forest	Randomforest-byhand.py
Gradient Boosting Machines	GradientBoostingMachines-byhand.py

Projects

Project	Dataset	Model	Code
Titanic Survival Prediction	Kaggle Titanic	Logistic Regression (from scratch)	log-reg-titanic.ipynb
Spam Detection	SMS Spam Collection	SVM (from scratch)	svm-spamdetection.ipynb

Visualisations

Regression

Model	Output
Simple Linear Regression
Multiple Linear Regression
Lasso Regression
Ridge & Lasso
ElasticNet

Classification

Model	Output
KNN